'''simple docstring'''




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class        a__ (  UpperCAmelCase__				,  unittest.TestCase					):
     lowerCamelCase    :	Tuple				=LDMTextToImagePipeline
     lowerCamelCase    :	List[Any]				=TEXT_TO_IMAGE_PARAMS - {
         "negative_prompt",
         "negative_prompt_embeds",
         "cross_attention_kwargs",
         "prompt_embeds",
     }
     lowerCamelCase    :	str				=PipelineTesterMixin.required_optional_params - {
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     lowerCamelCase    :	List[str]				=TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCamelCase    :	str				=False






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         torch.manual_seed(0       )
         __lowerCamelCase            =    UNetaDConditionModel(
             block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=4    ,							out_channels=4    ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,							cross_attention_dim=32    ,							)
         __lowerCamelCase            =    DDIMScheduler(
             beta_start=0.0_00_85    ,							beta_end=0.0_12    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=a    ,							set_alpha_to_one=a    ,							)
         torch.manual_seed(0       )
         __lowerCamelCase            =    AutoencoderKL(
             block_out_channels=(32, 64)    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')    ,							up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')    ,							latent_channels=4    ,							)
         torch.manual_seed(0       )
         __lowerCamelCase            =    CLIPTextConfig(
             bos_token_id=0    ,							eos_token_id=2    ,							hidden_size=32    ,							intermediate_size=37    ,							layer_norm_eps=1e-0_5    ,							num_attention_heads=4    ,							num_hidden_layers=5    ,							pad_token_id=1    ,							vocab_size=10_00    ,							)
         __lowerCamelCase            =    CLIPTextModel(a       )
         __lowerCamelCase            =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

         __lowerCamelCase            =    {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vqvae''': vae,
             '''bert''': text_encoder,
             '''tokenizer''': tokenizer,
         }
         return components






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						Dict    ,							a						:						Union[str, Any]=0       ):





         """simple docstring"""



         if str(a       ).startswith('''mps'''       ):
             __lowerCamelCase            =    torch.manual_seed(a       )
         else:
             __lowerCamelCase            =    torch.Generator(device=a       ).manual_seed(a       )
         __lowerCamelCase            =    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __lowerCamelCase            =    self.get_dummy_components()
         __lowerCamelCase            =    LDMTextToImagePipeline(**a       )
         pipe.to(a       )
         pipe.set_progress_bar_config(disable=a       )

         __lowerCamelCase            =    self.get_dummy_inputs(a       )
         __lowerCamelCase            =    pipe(**a       ).images
         __lowerCamelCase            =    image[0, -3:, -3:, -1]

         assert image.shape == (1, 16, 16, 3)
         __lowerCamelCase            =    np.array([0.61_01, 0.61_56, 0.56_22, 0.48_95, 0.66_61, 0.38_04, 0.57_48, 0.61_36, 0.50_14]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3




@slow
@require_torch_gpu
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							a						:						Dict    ,							a						:						int=torch.floataa    ,							a						:						Any=0       ):





         """simple docstring"""



         __lowerCamelCase            =    torch.manual_seed(a       )
         __lowerCamelCase            =    np.random.RandomState(a       ).standard_normal((1, 4, 32, 32)       )
         __lowerCamelCase            =    torch.from_numpy(a       ).to(device=a    ,							dtype=a       )
         __lowerCamelCase            =    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs






     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''       ).to(a       )
         pipe.set_progress_bar_config(disable=a       )

         __lowerCamelCase            =    self.get_inputs(a       )
         __lowerCamelCase            =    pipe(**a       ).images
         __lowerCamelCase            =    image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 2_56, 2_56, 3)
         __lowerCamelCase            =    np.array([0.5_18_25, 0.5_28_50, 0.5_25_43, 0.5_42_58, 0.5_23_04, 0.5_25_69, 0.5_43_63, 0.5_52_76, 0.5_68_78]       )
         __lowerCamelCase            =    np.abs(expected_slice - image_slice       ).max()
         assert max_diff < 1e-3




@nightly
@require_torch_gpu
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def   SCREAMING_SNAKE_CASE__  (					self						:						str    ,							a						:						Tuple    ,							a						:						Dict=torch.floataa    ,							a						:						int=0       ):





         """simple docstring"""



         __lowerCamelCase            =    torch.manual_seed(a       )
         __lowerCamelCase            =    np.random.RandomState(a       ).standard_normal((1, 4, 32, 32)       )
         __lowerCamelCase            =    torch.from_numpy(a       ).to(device=a    ,							dtype=a       )
         __lowerCamelCase            =    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 50,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''       ).to(a       )
         pipe.set_progress_bar_config(disable=a       )

         __lowerCamelCase            =    self.get_inputs(a       )
         __lowerCamelCase            =    pipe(**a       ).images[0]

         __lowerCamelCase            =    load_numpy(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''       )
         __lowerCamelCase            =    np.abs(expected_image - image       ).max()
         assert max_diff < 1e-3

'''simple docstring'''




def __lowerCAmelCase					(				)      ->       Optional[Any]:
    __lowerCamelCase            =    0
    for i in range(1							,    10_01							):
        total += i**i
    return str(UpperCamelCase__							)[-10:]


if __name__ == "__main__":
  print(solution())
